In the past, a typical application for copiers or scan-to-print image processing systems was to reproduce an input image as accurately as possible, i.e., render a copy. Thus, copies have been rendered as accurately as possible, flaws and all. However, as customers become more knowledgeable in their document reproduction requirements, they recognize that an exact copy is often not what they want. Instead, they would rather obtain the best possible document output. Until recently, image quality from the output of a copier or a scan-to-print system was directly related to the input document quality One very common set of input documents includes photographs. Unfortunately, photography is an inexact science, particularly among amateurs, and original photographs are often poor. Alternately, technology, age or image degradation variations often result in pictures having an unsatisfactory and undesirable appearance. What is desired then, is a copy giving the best possible picture, and not a copy of the original.
Photography has long dealt with this issue. Analog filters and illumination variations can improve the appearance of pictures in the analog photographic process. Thus, for example, yellow filters enhance the appearance of white clouds against a blue sky in alack and white images Further, various electrophotographic devices, including digital copiers, can clean up and improve images by adjustment of threshold, filtering, or background suppression. Generally, these methods are manual methods which a user must select on an image by image basis. Unfortunately, the casual user is not skilled enough to perform these operations. The inability to perform image enhancement operations is exacerbated when additionally dealing with color controls.
Three possible choices are presented by the art in the area of image enhancement. In the first case, we can do nothing. Such a system is a stable system, in that it does no harm to an image, but its output documents are sometimes not satisfactory to the ultimate customer. This is a common approach taken to reproduction.
In a second case of image enhancement, the image can always be processed. It turns out than an improvement can usually be made to an image if certain assumptions are made that are accurate for most cases. In an exceptionally large set of images, increasing contrast, sharpness, and/or color saturation, will improve the image. This model tends to produce better images, but the process is unstable, in that for multi-generation copying, increases in contrast, saturation, or sharpness are undesirable and ultimately lead to a severe image degradation. Further the process may undesirably operate on those images which are good ones.
Accordingly, we arrive at our third case of image enhancement, a process of automated image enhancement which operates to vary images which are not perceived as good images, but does not operate on images which do not need to be improved, thereby allowing a stable process.
One improvement that can be made to an image is to correct the image's use of an unbalanced distribution of density over the dynamic range of the image. This process is sometimes characterized as exposure adjustment. Digital input images directed to reproduction systems come from a variety of input sources such as copiers, slide scanners, flat-bed scanners, cameras, etc. In many cases, the description of the image will come from an unknown source, or one that might exhibit certain deficiencies. One common deficiency is that the digital representation of the image has an unbalanced distribution of density over the range of possible values, i.e., only covers a limited range of the possible values. This differs from contrast, which refers to the perception of the dynamic range of the image. Contrast adjustment will change the perceived contrast of the image, however it will only do a limited improvement on images that have exposure levels that are commonly described as "too light" or "too dark". In order to correct these kind of images, exposure and contrast have to be estimated and, when needed, enhanced. A number of contrast adjustments using the image histogram is known in the art. Histogram manipulation for the purpose of enhancing the appearance is described in R. C. Gonzales and B. A. Fittes, "Gray level transformation for interactive image enhancement," Proc. Second Conference on Remotely Manned Systems, 1975; E. L. Hall, "Almost uniform distributions for computer image enhancement," IEEE Trans. Comput. C-23, 207-208, 1974; W. K. Pratt, Digital Image Processing, Wiley, New York, 1978; and M. P. Ekstrom, Digital Image Processing Techniques, Academic Press, Orlando, 1984; J. C. Russ, The Image Processing Handbook, CRC Press Boca Raton, 1992.
Also noted is R. C. Gonzalez and P. Wintz, "Image Enhancement by Histogram Modification Techniques", Digital Image Processing, Addison-Wesley Publishing, 1977, p. 118 et seq., describing histogram flattening functions known in the art.
The references cited are herein incorporated by reference.
When images suffer from an unbalanced distribution of density over the dynamic range of the image, they may be characterized as having good contrast, but are either too light or too dark. Taking a dark image as an example, the image could be uniformly lightened in accordance with the function: EQU I'=I+.alpha. (1)
but that operation tends to lighten the background, and does not provide a desired improvement.
Alternatively, the dark image could be multiplied by a fixed value, in accordance with the function: EQU I'=.beta.I (2)
which would tend to linearly stretch the distribution of density over the dynamic range of the image.
As a third choice, the dark image could be altered in accordance with the function: EQU I'=I.sup..gamma. ( 3)
where 0.ltoreq.I.ltoreq.1 which tends to non-linearly alter the image.
The references identified are herein incorporated by reference for their teachings.